I. Field of the Invention
The present invention relates to the computerized monitoring of a system, and more specifically, to the automatic detection of system malfunctions through statistical analysis. The present invention particularly relates to such computerized monitoring in a medical environment.
II. Description of the Related Art
In a medical environment, health care providers often rely on computers for the purpose of monitoring one or more of a patient's characteristics. For example, U.S. Pat. No. 5,199,439, discloses a method of using medical and computer equipment to derive statistical charts which reflect changes in a monitored patient. Under this method, data from a monitored patient is collected in a time delayed manner, so that a computer is provided with several samples of data for each monitored event (e.g., blood pressure). When a statistically significant group of data is collected, the data is analyzed to determine "control limits," (e.g. at one standard deviation from normal) so that statistically significant deviations in the patient's condition can be monitored. Alternatively, the patient's data can be compared to data stored in a database to make the same determination. This method thus provides a physician an early warning system when the patient goes into an unstable condition, by indicating, e.g., that the patient's temperature has changed by a statistically significant amount.
Other art describes the use of computers to monitor a specific human system. U.S. Pat. No. 4,651,748, discloses a method for determining the state of a cardiovascular system by measuring and processing quantitative blood supply parameters and comparing these parameters with statistical average ranges established for the parameters. U.S. Pat. No. 5,027,817, discloses a method of presenting a statistically evaluated display which is obtained from Positron Emission Tomography (PET) scan data; a group of healthy subjects are used to provide reference data which is transformed into a Gaussian distribution for comparison with the patient's data. U.S. Pat. No. 4,534,388, discloses a method for monitoring the brain functions of a patient during a medical procedure relative to the patient's self-norm by taking a statistically significant sample of the patient's relevant brain functions prior to the initiation of a medical procedure, and comparing that sample with the patient's relevant brain functions during the procedure. U.S. Pat. No. 4,974,598, discloses a method in electrocardiographic (EKG) analysis for the early detection of certain heart diseases, by comparing EKG signals with the patient's self-norm "typical beat" and to data collected from a normal population group. The article by S. Pestotnik et al., "Therapeutic Antibiotic Monitoring: Surveillance Using a Computerized Expert System," The American Journal of Medicine, January 1990, Vol. 88, pp. 43-48, discloses a computerized system for monitoring therapeutic antibiotics in a hospital setting by generating a report alerting physicians that patient's antibiotic therapies are potentially inappropriate in light of current in vitro sensitivity data.
Health care providers also rely on computers to make daily decisions regarding health management. For example, the article by Hripcsak, G., et al., "The Arden Syntax for Medical Logic Modules," Proceedings of the Fourteenth Annual Symposium on Computer Applications in Medical Care, pp 200-204 (IEEE Computer Society Press, 1990) (incorporated herein by reference), describes a set of rules called Medical Logic Modules ("MLMs"), written in the Arden Syntax, which are used in a hospital's clinical event monitor. Whenever a medical event occurs (including administrative transactions like admissions, discharges, transfers, and outpatient visits; operative procedures; orders for items like medications and diagnostic tests; and the storage of clinical results of diagnostic tests), the MLMs that are pertinent to the event are triggered. The MLMs generally read data from the hospital patient database, test a set of criteria and, if those criteria are satisfied, performs some action such as sending a message via electronic-mail, storing a message in the patient database, or triggering other MLMS. MLMs typically generate drug contraindication warnings, abnormal value indications, laboratory interpretations, screening for research studies, reports for quality assurance, etc. However, as with any automated process, MLM's can and do generate erroneous messages.
There are several different ways that a malfunction can occur. A clinical information system may gather data from many independent departments, each of which has its own vocabulary, format, and policies. While this arrangement may have certain advantages, the lack of central control can lead to discrepancies. For example, when a change is made to one department's vocabulary, the change is not always reflected in the network linking the department to the MLM's, and consequently, a MLM's logic may not work correctly. A related problem occurs when an ancillary database management system changes. Sometimes whole systems stop working either accidentally or for maintenance. The MLMs themselves can be responsible for malfunctions. MLMs are written, maintained, and approved by a number of different persons, and they frequently undergo revision to change the criteria or edit the generated messages. It is easy for an inadvertent revision to cause the MLM's behavior to change in unexpected ways. Changes in care patterns or hospital policy can also cause the MLM to function incorrectly. Finally, malfunctions that have not yet been seen or imagined are likely to arise.
Malfunctions can undermine confidence in the computer-generated messages. Crying wolf once too often can damage credibility to the point that legitimate messages are ignored. It is therefore critical to avoid erroneous messages. Unfortunately, no system is perfect; when malfunctions do occur they must be recognized and fixed as quickly as possible. A malfunction can take the form of an erroneous message (false-positive message) or the more insidious failure to generate a message when it is legitimate (false-negative message).